A Technique for Low Cost Soil Mapping and Validation Using Expert Knowledge on a Watershed in Minas Gerais, Brazil

Silva, Sérgio Henrique Godinho; Owens, Phillip R.; Menezes, Michele Duarte de; Santos, Walbert Júnior Reis dos; Curi, Nilton

doi:10.2136/sssaj2013.09.0382

Cited by 20 publications

(4 citation statements)

References 30 publications

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“…It enables the designed sample distribution to closely replicate the covariate distribution (Minasny and McBratney, 2006). Conditioned Latin hypercube sampling is thus considered as an efficient sampling method and implemented widely (Worsham et al, 2012;Louis et al, 2014;TaghizadehMehrjardi et al, 2014;Pahlavan Rad et al, 2014;Silva et al, 2014Silva et al, , 2015Kidd et al, 2015).…”

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confidence: 99%

Evaluation of Integrative Hierarchical Stepwise Sampling for Digital Soil Mapping

Yang

Feng

Zhu

et al. 2016

Soil Science Soc of Amer J

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This paper presents an integrative hierarchical stepwise sampling (IHS) method and two case studies to compare it with stratified random sampling (SRS) and conditioned Latin hypercube sampling (cLHS). The first comparison between IHS and SRS was conducted for mapping sand content of two soil layers in a study area in Anhui Province, China. Two sample sets of the same sample size were collected in the field based on IHS and SRS. The second case study is a simulation study, where we compared IHS and cLHS for mapping soil series in the Raffelson watershed in Wisconsin (USA). The study used an accurate and detailed soil series map produced previously as a proxy of the real soil distribution. Virtual samples with nine sample sizes designed by IHS and cLHS were collected on the soil map. For both case studies, an individual predictive soil mapping method was employed and independent validation samples were used to evaluate the mapping accuracies. Results indicate that IHS generally performs better than SRS for capturing distributions of the environmental variables. It obtained higher mapping accuracies than SRS at different sample sizes. On the other hand, cLHS appears to provide a better representation for distributions of the environmental variables than IHS, but the mapping accuracies with IHS are higher than those with cLHS at nearly all sample sizes. Finally, both case studies showed that IHS provides valuable information on representativeness of the samples.Abbreviations: FCM, fuzzy c-means; IHS, integrative hierarchical stepwise sampling; cLHS, conditioned Latin hypercube sampling; SRS, stratified random sampling. S oil sampling plays an essential role in soil survey. A sampling design specifies where and how many to sample in the field. Different sampling design strategies will produce different soil distribution maps and greatly affect the mapping accuracies (Brus and de Gruijter, 1997;van Groenigen et al., 2000;de Gruijter et al., 2006;Brus et al., 2006;Gregoire and Valentine, 2007;Heim et al., 2009). Because the collection of soil samples in the field is often time-consuming and resource-intensive, it is important to apply efficient sampling design strategies.When prior knowledge on an area's local soil variation is limited, simple random sampling has been a commonly used sampling strategy (Oliver and Webster, 1986;Grinand et al., 2008;Walvoort et al., 2010 • Evaluation of an integrative hierarchical stepwise (IHS) sampling method by comparing it with stratified random sampling (SRS) and conditioned Latin hypercube sampling (cLHS) through two case studies.• IHS obtained higher mapping accuracies than SRS and cLHS at nearly all sample sizes.• IHS provides valuable information on the representativeness of samples.• SRS and cLHS were found to generate unstable results on sample sets and soil maps.

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confidence: 99%

Evaluation of Integrative Hierarchical Stepwise Sampling for Digital Soil Mapping

Yang

Feng

Zhu

et al. 2016

Soil Science Soc of Amer J

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“…Such tacit knowledge is mostly based on a paradigm of conceptual soil-landscape model, under the hypothesis that the location and distribution of soils in the landscape is predictable 11 . Thus, the use of spatial inference models as fuzzy logics 12 , or bayesian inference 13 , to name a few, take advantage of such concepts in predictive maps.…”

Section: Introductionmentioning

confidence: 99%

Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil

Silva

Avalos

et al. 2019

Sci Rep

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This study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha). A soil survey was carried out and 74 soil profiles were analyzed. The tested models were: Multinomial logistic regression (MLR), C5 decision tree (C5-DT) and Random forest (RF). In order to reduce the effects of an imbalanced dataset on the accuracy of the tested models, additional sampling retrieved by photointerpretation was necessary. Accuracy assessment was based on aggregated data from a proportional 5-fold cross-validation procedure. Extrapolation assessment was based on the multivariate environmental similarity surface (MESS). The RF model including additional sampling (RF*) showed the best performance among the tested models (overall accuracy = 49%, kappa index = 0.33). The RF* allowed to link soil mapping units (SMU) and, in the case of less-common soil classes in the watershed, to set specific conditions of occurrence on the space of terrain-attributes. MESS analysis showed reliable outputs for 82.5% of the watershed. SMU distribution across the watershed was: Typic Rhodudult (56%), Typic Hapludult* (13%), Typic Dystrudept (10%), Typic Endoaquent + Fluventic Dystrudept (10%), Typic Hapludult (9.5%) and Rhodic Hapludox + Typic Hapludox (2%).

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“…Results showed that optimized networks are representative of the whole study area and they well‐capture the overall seasonality of precipitation in the region according to the TRMM climatology. This result agrees with other studies which highlight the ability of cLHS to capture the variability of multiple input covariates with a limited number of samples (Brungard and Boettinger, ; Mulder et al ., ; Godinho Silva et al ., ; Levi and Rasmussen, ; Ramirez‐Lopez et al ., ; Yin et al ., ; ; Domenech et al ., ). Although a less representative sampling is expected under accessibility restrictions (Roudier et al ., ; Godinho Silva et al ., ; Yin et al ., ), no major difference were observed in the precipitation distribution captured by both networks.…”

Section: Discussionmentioning

confidence: 99%

Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

Contreras

Ballari

Bruin

et al. 2018

Intl Journal of Climatology

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Rain gauge networks are crucial for enhancing the spatio-temporal characterization of precipitation. In tropical regions, scarcity of rain gauge data, climatic variability, and variable spatial accessibility make conventional approaches to design rain gauge networks inadequate and impractical. In this study, we propose the use of conditioned Latin hypercube sampling (cLHS) method with multi-temporal layers of remotely sensed precipitation measurements for capturing the spatio-temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon region of Ecuador, for which monthly precipitation averages were derived based on a 16-year period of Tropical Rainfall Measuring Mission (TRMM 3B43 V7) data which were used as prior information to select representative sampling points through cLHS. Two scenarios for the sampling design were considered and evaluated, one without and one with restrictions on accessible sites according to the proximity to roads and settlements. Results showed that both optimized networks captured the variability of precipitation according to the TRMM climatology. Furthermore, evaluation against an independent satellite precipitation data set showed that the optimized networks support mapping precipitation based on ordinary kriging (OK). Comparison with regular and random sampling methods showed that particularly when a practical scenario is considered, the optimized network provided more reliable results over time, highlighting the suitability of the network to capture temporal changes and map precipitation with high accuracy. The proposed approach could be easily adopted in other ungauged and poorly accessible regions for rain gauge network design as well as to the design of multi-objective monitoring networks.

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A Technique for Low Cost Soil Mapping and Validation Using Expert Knowledge on a Watershed in Minas Gerais, Brazil

Cited by 20 publications

References 30 publications

Evaluation of Integrative Hierarchical Stepwise Sampling for Digital Soil Mapping

Evaluation of Integrative Hierarchical Stepwise Sampling for Digital Soil Mapping

Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil

Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

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